Master the Most Important Deep Learning Frameworks for Python Data Science

This course is your complete guide to practical machine and deep learning using the Tensorflow and Keras frameworks in Python. In the age of Big Data, companies across the globe use Python to sift through the avalanche of information at their disposal and the advent of Tensorflow and Keras is revolutionizing deep learning. This course will help you break into this booming field.

Access 62 lectures & 5 hours of content 24/7

Get a full introduction to Python Data Science

Get started w/ Jupyter notebooks for implementing data science techniques in Python

Learn about Tensorflow & Keras installation

Understand the workings of Pandas & Numpy

Cover the basics of the Tensorflow syntax & graphing environment and Keras syntax

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Tensorflow Bootcamp For Data Science In Python

This course is your complete guide to practical data science using the Tensorflow framework in Python. Here, you'll cover all the aspects of practical data science with Tensorflow, Google's powerful deep learning framework used by organizations everywhere.

Access 62 lectures & 5 hours of content 24/7

Get a full introduction to Python Data Science

Get started w/ Jupyter notebooks for implementing data science techniques in Python

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Python Regression Analysis: Statistics & Machine Learning

This course offers a complete guide to practical data science using Python. You'll cover all aspects of practical data science in Python. By storing, filtering, managing, and manipulating data in Python, you can giver your company a competitive edge and boost your career to the next level.

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Important Details

Length of time users can access this course: lifetime

Access options: web streaming, mobile streaming

Certification of completion included

Redemption deadline: redeem your code within 30 days of purchase

Experience level required: beginner

Requirements

Internet required

Course Outline

Introduction to the Data Science in Python Bootcamp

Welcome to the Course - 1:40

Data and Scripts for the Course

Introduction to the Python Data Science Tool - 10:57

For Mac Users - 4:05

Introduction to the Python Data Science Environment - 19:15

Some Miscellaneous IPython Usage Facts - 5:25

Online iPython Interpreter - 3:26

Conclusion to Section 1 - 2:36

Introduction to Pandas

What are Pandas? - 12:06

Read CSV Data in Python - 5:42

Read in Excel File - 5:31

Read HTML Data - 12:06

Read JSON Data - 9:14

Conclusions to Section 4 - 2:06

Data Pre-Processing/Wrangling

Remove NA Values - 10:28

Basic Data Handling: Starting with Conditional Data Selection - 5:24

Basic Data Grouping Based on Qualitative Attributes - 9:47

Rank and Sort Data - 8:03

Concatenate - 8:16

Merge - 10:47

Basic Statistical Data Analysis

What is Statistical Data Analysis? - 10:08

Some Pointers on Collecting Data for Statistical Studies - 8:38

Explore the Quantitative Data: Descriptive Statistics - 9:05

Group By Qualitative Categories - 10:25

Visualize Descriptive Statistics-Boxplots - 5:28

Common Terms Relating to Descriptive Statistics - 5:15

Data Distribution- Normal Distribution - 4:07

Check for Normal Distribution - 6:23

Standard Normal Distribution and Z-scores - 4:10

Confidence Interval-Theory - 6:06

Confidence Interval-Calculation - 5:20

Regression Modelling for Defining Relationship bw Variables

Explore the Relationship Between Two Quantitative Variables - 4:26

Correlation Analysis - 8:26

Linear Regression-Theory - 10:44

Linear Regression-Implementation in Python - 11:18

Conditions of Linear Regression-Check in Python - 12:03

Polynomial Regression - 3:53

GLM: Generalized Linear Model - 5:25

Logistic Regression - 11:10

Machine Learning for Data Science

How is Machine Learning Different from Statistical Data Analysis? - 5:36

What is Machine Learning (ML) About? Some Theoretical Pointers - 5:32

Machine Learning Based Regression Modelling

What is this section about - 10:10

Data Preparation for Supervised Learning - 9:47

Pointers on Evaluating the Accuracy of Classification and Regression Modelling - 9:42

Complete Data Science Training with Python for Data Analysis

Learn Statistics, Visualization, Machine Learning & More

In this easy-to-understand, hands-on course, you'll learn the most valuable Python Data Science basics and techniques. You'll discover how to implement these methods using real data obtained from different sources and get familiar with packages like Numpy, Pandas, Matplotlib, and more. You'll even understand deep concepts like statistical modeling in Python's Statsmodels package and the difference between statistics and machine learning.

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Complete Time Series Data Analysis Bootcamp In R

Learn How To Work with Temporal Data Using Statistical Modeling & Machine Learning Techniques In R

In this course, you'll use easy-to-understand, hands-on methods to absorb the most valuable R Data Science basics and techniques. After this course, you'll understand the underlying concepts to understand what algorithms and methods are best-suited for your data.

Access 52 lectures & 5 hours of content 24/7

Get an introduction to powerful R-based packages for time series analysis

Learn commonly used techniques, visualization methods & machine/deep learning techniques that can be implemented for time series data

Apply these frameworks to real life data including temporal stocks & financial data

Instructor

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Practical Neural Networks & Deep Learning In R

Dive into R data science using real data in this comprehensive, hands-on course. Get up to speed with data science packages like caret, h20, MXNET, as well as underlying concepts like which algorithms and methods are best suited for different kinds of data. Help your company scale by becoming an R expert!

Access 51 lectures & 5 hours of content 24/7

Get introduced to powerful R-based deep learning packages such as h2o & MXNET

Learn to apply these frameworks to real life data for classification & regression applications

Instructor

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Clustering & Classification with R

Harness The Power of Machine Learning For Unsupervised & Supervised Learning In R

In this course, you'll learn to implement R methods using real data obtained from different sources. After this course, you'll understand concepts like unsupervised learning, dimension reduction, and supervised learning.

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

Clustering & Classification with Machine Learning In Python

Harness The Power of Machine Learning For Unsupervised & Supervised Learning In Python

In this course, you’ll start by absorbing the most valuable Python Data Science basics and techniques. You'll get up to speed with packages like Numpy, Pandas, and Matplotlib and work with real data in Python. You'll even delve into concepts like unsupervised learning, dimension reduction, and supervised learning.

Access 46 lectures & 4 hours of content 24/7

Harness the power of Anaconda/iPython for practical data science

Carry out basic data pre-processing & wrangling in Python

Implement dimensional reduction techniques (PCA) & feature selection

Explore neural network & deep learning based classification

Instructor

Minerva Singh is a PhD graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.